Element analysis results
# Example of how to use the function with your data
results <- analyze_elements(long_dat)
# Example of how to access results for a specific element
results[["Ph"]]$SummaryStats
## # A tibble: 2 × 4
## WormPresence Mean SD Median
## <dbl> <dbl> <dbl> <dbl>
## 1 0 7.17 0.209 7.21
## 2 1 7.18 0.224 7.16
results[["Ph"]]$ANOVA
## # A tibble: 2 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 0.00125 0.00125 0.0267 0.871
## 2 Residuals 52 2.43 0.0468 NA NA
results[["SS"]]$SummaryStats
## # A tibble: 2 × 4
## WormPresence Mean SD Median
## <dbl> <dbl> <dbl> <dbl>
## 1 0 0.210 0.107 0.19
## 2 1 0.200 0.0303 0.19
results[["SS"]]$ANOVA
## # A tibble: 2 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 0.00135 0.00135 0.219 0.642
## 2 Residuals 52 0.320 0.00616 NA NA
results[["NitN"]]$SummaryStats
## # A tibble: 2 × 4
## WormPresence Mean SD Median
## <dbl> <dbl> <dbl> <dbl>
## 1 0 0.0178 0.0169 0.01
## 2 1 0.0230 0.0441 0.01
results[["NitN"]]$ANOVA
## # A tibble: 2 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 0.000363 0.000363 0.325 0.571
## 2 Residuals 52 0.0580 0.00112 NA NA
results[["AmoN"]]$SummaryStats
## # A tibble: 2 × 4
## WormPresence Mean SD Median
## <dbl> <dbl> <dbl> <dbl>
## 1 0 0.309 0.257 0.24
## 2 1 0.275 0.266 0.17
results[["AmoN"]]$ANOVA
## # A tibble: 2 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 0.0157 0.0157 0.229 0.634
## 2 Residuals 52 3.56 0.0684 NA NA
results[["CA"]]$SummaryStats
## # A tibble: 2 × 4
## WormPresence Mean SD Median
## <dbl> <dbl> <dbl> <dbl>
## 1 0 14.7 6.49 13.4
## 2 1 18.8 5.63 20
results[["CA"]]$ANOVA
## # A tibble: 2 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 219. 219. 5.93 0.0184
## 2 Residuals 52 1920. 36.9 NA NA
results[["Po"]]$SummaryStats
## # A tibble: 2 × 4
## WormPresence Mean SD Median
## <dbl> <dbl> <dbl> <dbl>
## 1 0 13.1 6.17 12.3
## 2 1 10.8 4.36 10.0
results[["Po"]]$ANOVA
## # A tibble: 2 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 72.0 72.0 2.52 0.119
## 2 Residuals 52 1487. 28.6 NA NA
results[["Mag"]]$SummaryStats
## # A tibble: 2 × 4
## WormPresence Mean SD Median
## <dbl> <dbl> <dbl> <dbl>
## 1 0 2.41 0.801 2.24
## 2 1 2.87 0.705 3
results[["Mag"]]$ANOVA
## # A tibble: 2 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 2.86 2.86 5.02 0.0293
## 2 Residuals 52 29.6 0.570 NA NA
results[["Pho"]]$SummaryStats
## # A tibble: 2 × 4
## WormPresence Mean SD Median
## <dbl> <dbl> <dbl> <dbl>
## 1 0 2.57 2.82 1.72
## 2 1 1.92 2.83 0.906
results[["Pho"]]$ANOVA
## # A tibble: 2 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 5.70 5.70 0.715 0.402
## 2 Residuals 52 415. 7.97 NA NA
results[["AL"]]$SummaryStats
## # A tibble: 2 × 4
## WormPresence Mean SD Median
## <dbl> <dbl> <dbl> <dbl>
## 1 0 0.114 0.0334 0.109
## 2 1 0.113 0.0334 0.101
results[["AL"]]$ANOVA
## # A tibble: 2 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 0.0000190 0.0000190 0.0170 0.897
## 2 Residuals 52 0.0579 0.00111 NA NA
results[["BO"]]$SummaryStats
## # A tibble: 2 × 4
## WormPresence Mean SD Median
## <dbl> <dbl> <dbl> <dbl>
## 1 0 0.0406 0.0193 0.034
## 2 1 0.0369 0.0180 0.031
results[["BO"]]$ANOVA
## # A tibble: 2 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 0.000189 0.000189 0.543 0.465
## 2 Residuals 52 0.0181 0.000348 NA NA
results[["CO"]]$SummaryStats
## # A tibble: 2 × 4
## WormPresence Mean SD Median
## <dbl> <dbl> <dbl> <dbl>
## 1 0 0.00565 0.00394 0.005
## 2 1 0.00561 0.00307 0.006
results[["CO"]]$ANOVA
## # A tibble: 2 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 0.0000000150 0.0000000150 0.00120 0.972
## 2 Residuals 52 0.000648 0.0000125 NA NA
results[["IR"]]$SummaryStats
## # A tibble: 2 × 4
## WormPresence Mean SD Median
## <dbl> <dbl> <dbl> <dbl>
## 1 0 0.0347 0.0113 0.033
## 2 1 0.0298 0.0100 0.029
results[["IR"]]$ANOVA
## # A tibble: 2 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 0.000328 0.000328 2.88 0.0958
## 2 Residuals 52 0.00592 0.000114 NA NA
results[["Man"]]$SummaryStats
## # A tibble: 2 × 4
## WormPresence Mean SD Median
## <dbl> <dbl> <dbl> <dbl>
## 1 0 0.00122 0.00318 0
## 2 1 0.000222 0.000424 0
results[["Man"]]$ANOVA
## # A tibble: 2 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 0.0000135 0.0000135 2.63 0.111
## 2 Residuals 52 0.000267 0.00000514 NA NA
results[["MO"]]$SummaryStats
## # A tibble: 2 × 4
## WormPresence Mean SD Median
## <dbl> <dbl> <dbl> <dbl>
## 1 0 0.0127 0.0282 0.0049
## 2 1 0.00525 0.00490 0.005
results[["MO"]]$ANOVA
## # A tibble: 2 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 0.000755 0.000755 1.85 0.180
## 2 Residuals 52 0.0213 0.000409 NA NA
results[["SO"]]$SummaryStats
## # A tibble: 2 × 4
## WormPresence Mean SD Median
## <dbl> <dbl> <dbl> <dbl>
## 1 0 20.5 3.17 20.3
## 2 1 21.9 3.70 21
results[["SO"]]$ANOVA
## # A tibble: 2 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 25.7 25.7 2.16 0.147
## 2 Residuals 52 618. 11.9 NA NA
results[["SU"]]$SummaryStats
## # A tibble: 2 × 4
## WormPresence Mean SD Median
## <dbl> <dbl> <dbl> <dbl>
## 1 0 6.16 2.83 5.51
## 2 1 5.17 2.57 4.92
results[["SU"]]$ANOVA
## # A tibble: 2 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 13.2 13.2 1.81 0.184
## 2 Residuals 52 380. 7.30 NA NA
results[["Zi"]]$SummaryStats
## # A tibble: 2 × 4
## WormPresence Mean SD Median
## <dbl> <dbl> <dbl> <dbl>
## 1 0 0.0620 0.0463 0.045
## 2 1 0.056 0.0274 0.05
results[["Zi"]]$ANOVA
## # A tibble: 2 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 0.000492 0.000492 0.340 0.562
## 2 Residuals 52 0.0752 0.00145 NA NA
# ------------------------------------------------------------------------------------
# ------------------------------------------------------------------------------------
# ------------------------------------------------------------------------------------
analyze_elements_detailed <- function(data) {
results <- list() # Create an empty list to store results
# Loop over each element
unique_elements <- unique(data$element)
for (elem in unique_elements) {
# Filter data for the specific element
elem_data <- data %>% filter(element == elem)
# Calculate summary statistics
summary_stats <- elem_data %>%
group_by(Plant, WormPresence) %>%
summarise(
Mean = mean(amount, na.rm = TRUE),
SD = sd(amount, na.rm = TRUE),
Median = median(amount, na.rm = TRUE),
.groups = 'drop'
)
# Perform ANOVA to compare groups including interaction terms
if (n_distinct(elem_data$WormPresence) > 1 && n_distinct(elem_data$Plant) > 1) {
aov_results <- aov(amount ~ WormPresence * Plant, data = elem_data)
tidy_aov <- tidy(aov_results)
} else {
tidy_aov <- tibble(term = NA, estimate = NA, statistic = NA, p.value = NA)
}
# Store results in the list
results[[elem]] <- list(
SummaryStats = summary_stats,
ANOVA = tidy_aov
)
}
return(results)
}
# Example of how to use the function with your data
results_detailed <- analyze_elements_detailed(long_dat)
# Example of how to access results for a specific element
if (!is.null(results_detailed[["Ph"]])) {
print(results_detailed[["Ph"]]$SummaryStats)
print(results_detailed[["Ph"]]$ANOVA)
}
## # A tibble: 14 × 5
## Plant WormPresence Mean SD Median
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Chives 0 7.24 0.0971 7.22
## 2 Chives 1 7.09 0.0651 7.09
## 3 Cilantro 0 7.25 0.115 7.32
## 4 Cilantro 1 7.50 0.0306 7.49
## 5 Larkspur 0 7.23 0.0871 7.24
## 6 Larkspur 1 7.21 0.193 7.32
## 7 Mulch 0 6.86 0.279 6.75
## 8 Mulch 1 6.88 0.213 6.86
## 9 Onion 0 7.23 0.125 7.29
## 10 Onion 1 7.25 0.154 7.27
## 11 Scallion 0 7.28 0.153 7.2
## 12 Scallion 1 7.17 0.0603 7.16
## 13 Whitelace 0 7.23 0.0833 7.26
## 14 Whitelace 1 7.31 0.0737 7.28
## # A tibble: 4 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 0.00125 0.00125 0.0519 0.821
## 2 Plant 6 1.32 0.219 9.09 0.00000280
## 3 WormPresence:Plant 6 0.154 0.0257 1.06 0.400
## 4 Residuals 40 0.965 0.0241 NA NA
if (!is.null(results_detailed[["SS"]])) {
print(results_detailed[["SS"]]$SummaryStats)
print(results_detailed[["SS"]]$ANOVA)
}
## # A tibble: 14 × 5
## Plant WormPresence Mean SD Median
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Chives 0 0.22 0.0361 0.23
## 2 Chives 1 0.197 0.0208 0.19
## 3 Cilantro 0 0.19 0.0100 0.19
## 4 Cilantro 1 0.203 0.0321 0.19
## 5 Larkspur 0 0.29 0.229 0.19
## 6 Larkspur 1 0.202 0.0130 0.2
## 7 Mulch 0 0.15 0.0464 0.15
## 8 Mulch 1 0.17 0.0283 0.15
## 9 Onion 0 0.18 0.0212 0.18
## 10 Onion 1 0.198 0.0303 0.19
## 11 Scallion 0 0.207 0.0473 0.19
## 12 Scallion 1 0.22 0.0361 0.23
## 13 Whitelace 0 0.243 0.0666 0.21
## 14 Whitelace 1 0.233 0.0289 0.25
## # A tibble: 4 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 0.00135 0.00135 0.215 0.646
## 2 Plant 6 0.0472 0.00786 1.25 0.302
## 3 WormPresence:Plant 6 0.0213 0.00355 0.565 0.756
## 4 Residuals 40 0.252 0.00629 NA NA
if (!is.null(results_detailed[["NitN"]])) {
print(results_detailed[["NitN"]]$SummaryStats)
print(results_detailed[["NitN"]]$ANOVA)
}
## # A tibble: 14 × 5
## Plant WormPresence Mean SD Median
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Chives 0 0.0133 0.00577 0.01
## 2 Chives 1 0.01 0 0.01
## 3 Cilantro 0 0.0333 0.0115 0.04
## 4 Cilantro 1 0.103 0.112 0.06
## 5 Larkspur 0 0.008 0.00837 0.01
## 6 Larkspur 1 0.01 0.0141 0
## 7 Mulch 0 0.028 0.0295 0.03
## 8 Mulch 1 0.006 0.00894 0
## 9 Onion 0 0.006 0.00548 0.01
## 10 Onion 1 0.006 0.00548 0.01
## 11 Scallion 0 0.03 0.01 0.03
## 12 Scallion 1 0.0267 0.0153 0.03
## 13 Whitelace 0 0.0133 0.00577 0.01
## 14 Whitelace 1 0.03 0.01 0.03
## # A tibble: 4 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 0.000363 0.000363 0.465 0.499
## 2 Plant 6 0.0181 0.00302 3.87 0.00391
## 3 WormPresence:Plant 6 0.00866 0.00144 1.85 0.114
## 4 Residuals 40 0.0313 0.000781 NA NA
if (!is.null(results_detailed[["AmoN"]])) {
print(results_detailed[["AmoN"]]$SummaryStats)
print(results_detailed[["AmoN"]]$ANOVA)
}
## # A tibble: 14 × 5
## Plant WormPresence Mean SD Median
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Chives 0 0.07 0.0794 0.04
## 2 Chives 1 0.0767 0.0833 0.05
## 3 Cilantro 0 0.253 0.172 0.18
## 4 Cilantro 1 0.153 0.131 0.11
## 5 Larkspur 0 0.418 0.259 0.27
## 6 Larkspur 1 0.414 0.304 0.45
## 7 Mulch 0 0.374 0.345 0.13
## 8 Mulch 1 0.338 0.383 0.17
## 9 Onion 0 0.408 0.329 0.34
## 10 Onion 1 0.344 0.330 0.2
## 11 Scallion 0 0.233 0.230 0.24
## 12 Scallion 1 0.167 0.0379 0.15
## 13 Whitelace 0 0.227 0.0907 0.24
## 14 Whitelace 1 0.253 0.163 0.18
## # A tibble: 4 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 0.0157 0.0157 0.218 0.643
## 2 Plant 6 0.666 0.111 1.55 0.188
## 3 WormPresence:Plant 6 0.0206 0.00344 0.0479 0.999
## 4 Residuals 40 2.87 0.0718 NA NA
if (!is.null(results_detailed[["CA"]])) {
print(results_detailed[["CA"]]$SummaryStats)
print(results_detailed[["CA"]]$ANOVA)
}
## # A tibble: 14 × 5
## Plant WormPresence Mean SD Median
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Chives 0 18.6 1.88 17.6
## 2 Chives 1 22.4 3.33 21.4
## 3 Cilantro 0 16.8 2.34 16.6
## 4 Cilantro 1 16.6 2.12 17.1
## 5 Larkspur 0 12.9 1.75 12.5
## 6 Larkspur 1 19.0 2.08 20
## 7 Mulch 0 8.73 6.56 5.38
## 8 Mulch 1 11.8 6.75 7.74
## 9 Onion 0 13.1 3.91 12
## 10 Onion 1 19.1 5.72 21.0
## 11 Scallion 0 15.5 6.58 13.3
## 12 Scallion 1 24.6 3.07 24.0
## 13 Whitelace 0 23.8 10.6 19.6
## 14 Whitelace 1 22.2 2.45 22.3
## # A tibble: 4 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 219. 219. 9.05 0.00454
## 2 Plant 6 816. 136. 5.63 0.000263
## 3 WormPresence:Plant 6 136. 22.7 0.938 0.479
## 4 Residuals 40 968. 24.2 NA NA
if (!is.null(results_detailed[["Po"]])) {
print(results_detailed[["Po"]]$SummaryStats)
print(results_detailed[["Po"]]$ANOVA)
}
## # A tibble: 14 × 5
## Plant WormPresence Mean SD Median
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Chives 0 11.0 3.52 9.22
## 2 Chives 1 5.38 2.28 5.16
## 3 Cilantro 0 11.8 0.526 11.9
## 4 Cilantro 1 10.3 3.85 11.7
## 5 Larkspur 0 15.3 6.03 15.2
## 6 Larkspur 1 12.3 3.47 11.2
## 7 Mulch 0 9.70 7.89 8.31
## 8 Mulch 1 12.4 3.77 10.3
## 9 Onion 0 14.0 8.87 11.0
## 10 Onion 1 12.1 7.12 12.1
## 11 Scallion 0 12.0 1.94 12.5
## 12 Scallion 1 9.58 2.91 8.91
## 13 Whitelace 0 17.8 6.10 15.4
## 14 Whitelace 1 10.1 1.12 10.0
## # A tibble: 4 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 72.0 72.0 2.43 0.127
## 2 Plant 6 178. 29.7 1.01 0.436
## 3 WormPresence:Plant 6 125. 20.8 0.704 0.648
## 4 Residuals 40 1183. 29.6 NA NA
if (!is.null(results_detailed[["Mag"]])) {
print(results_detailed[["Mag"]]$SummaryStats)
print(results_detailed[["Mag"]]$ANOVA)
}
## # A tibble: 14 × 5
## Plant WormPresence Mean SD Median
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Chives 0 2.88 0.275 2.78
## 2 Chives 1 3.29 0.504 3.16
## 3 Cilantro 0 2.63 0.367 2.66
## 4 Cilantro 1 2.80 0.316 2.76
## 5 Larkspur 0 2.09 0.300 2
## 6 Larkspur 1 2.54 0.450 2.70
## 7 Mulch 0 1.83 0.745 1.65
## 8 Mulch 1 2.23 0.706 1.83
## 9 Onion 0 2.16 0.509 2
## 10 Onion 1 2.80 0.772 2.91
## 11 Scallion 0 2.49 0.828 2.29
## 12 Scallion 1 3.47 0.504 3.26
## 13 Whitelace 0 3.57 1.40 2.95
## 14 Whitelace 1 3.66 0.449 3.84
## # A tibble: 4 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 2.86 2.86 7.10 0.0110
## 2 Plant 6 12.7 2.11 5.24 0.000460
## 3 WormPresence:Plant 6 0.827 0.138 0.342 0.910
## 4 Residuals 40 16.1 0.403 NA NA
if (!is.null(results_detailed[["Pho"]])) {
print(results_detailed[["Pho"]]$SummaryStats)
print(results_detailed[["Pho"]]$ANOVA)
}
## # A tibble: 14 × 5
## Plant WormPresence Mean SD Median
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Chives 0 1.45 1.04 1.69
## 2 Chives 1 1.35 0.998 1.01
## 3 Cilantro 0 3.79 1.49 3.25
## 4 Cilantro 1 0.943 0.962 0.906
## 5 Larkspur 0 2.45 0.464 2.5
## 6 Larkspur 1 1.29 1.32 0.838
## 7 Mulch 0 0.604 0.314 0.7
## 8 Mulch 1 0.389 0.357 0.285
## 9 Onion 0 2.47 1.98 1.71
## 10 Onion 1 2.07 2.16 0.985
## 11 Scallion 0 0.935 0.790 1.02
## 12 Scallion 1 0.693 0.894 0.225
## 13 Whitelace 0 7.77 5.68 5.79
## 14 Whitelace 1 8.07 4.52 6.50
## # A tibble: 4 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 5.70 5.70 1.41 0.242
## 2 Plant 6 242. 40.4 9.97 0.00000101
## 3 WormPresence:Plant 6 10.6 1.76 0.435 0.851
## 4 Residuals 40 162. 4.05 NA NA
if (!is.null(results_detailed[["AL"]])) {
print(results_detailed[["AL"]]$SummaryStats)
print(results_detailed[["AL"]]$ANOVA)
}
## # A tibble: 14 × 5
## Plant WormPresence Mean SD Median
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Chives 0 0.094 0.0229 0.099
## 2 Chives 1 0.124 0.0484 0.101
## 3 Cilantro 0 0.108 0.0121 0.109
## 4 Cilantro 1 0.124 0.0331 0.117
## 5 Larkspur 0 0.127 0.0458 0.109
## 6 Larkspur 1 0.114 0.0254 0.104
## 7 Mulch 0 0.139 0.0394 0.144
## 8 Mulch 1 0.121 0.0322 0.103
## 9 Onion 0 0.111 0.0183 0.1
## 10 Onion 1 0.110 0.0153 0.1
## 11 Scallion 0 0.121 0.00954 0.116
## 12 Scallion 1 0.131 0.0422 0.136
## 13 Whitelace 0 0.0733 0.0240 0.083
## 14 Whitelace 1 0.0603 0.0142 0.063
## # A tibble: 4 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 0.0000190 0.0000190 0.0204 0.887
## 2 Plant 6 0.0174 0.00290 3.12 0.0133
## 3 WormPresence:Plant 6 0.00342 0.000570 0.615 0.717
## 4 Residuals 40 0.0371 0.000928 NA NA
if (!is.null(results_detailed[["BO"]])) {
print(results_detailed[["BO"]]$SummaryStats)
print(results_detailed[["BO"]]$ANOVA)
}
## # A tibble: 14 × 5
## Plant WormPresence Mean SD Median
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Chives 0 0.0233 0.00451 0.023
## 2 Chives 1 0.0203 0.00208 0.021
## 3 Cilantro 0 0.0457 0.0102 0.05
## 4 Cilantro 1 0.0293 0.00611 0.028
## 5 Larkspur 0 0.0322 0.0160 0.028
## 6 Larkspur 1 0.0382 0.0208 0.032
## 7 Mulch 0 0.0578 0.00867 0.058
## 8 Mulch 1 0.0472 0.0226 0.038
## 9 Onion 0 0.0474 0.0330 0.029
## 10 Onion 1 0.038 0.0239 0.03
## 11 Scallion 0 0.036 0.00624 0.038
## 12 Scallion 1 0.0407 0.0163 0.044
## 13 Whitelace 0 0.0317 0.0124 0.025
## 14 Whitelace 1 0.036 0.01 0.036
## # A tibble: 4 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 0.000189 0.000189 0.573 0.453
## 2 Plant 6 0.00405 0.000674 2.05 0.0818
## 3 WormPresence:Plant 6 0.000877 0.000146 0.444 0.845
## 4 Residuals 40 0.0132 0.000330 NA NA
if (!is.null(results_detailed[["CO"]])) {
print(results_detailed[["CO"]]$SummaryStats)
print(results_detailed[["CO"]]$ANOVA)
}
## # A tibble: 14 × 5
## Plant WormPresence Mean SD Median
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Chives 0 0.00167 0.00289 0
## 2 Chives 1 0.005 0 0.005
## 3 Cilantro 0 0.012 0.00173 0.011
## 4 Cilantro 1 0.00767 0.00208 0.007
## 5 Larkspur 0 0.00756 0.00293 0.008
## 6 Larkspur 1 0.00456 0.00269 0.0049
## 7 Mulch 0 0.00536 0.00116 0.0049
## 8 Mulch 1 0.00298 0.00281 0.004
## 9 Onion 0 0.00498 0.00324 0.005
## 10 Onion 1 0.00678 0.00134 0.007
## 11 Scallion 0 0 0 0
## 12 Scallion 1 0.00433 0.00404 0.005
## 13 Whitelace 0 0.00733 0.00153 0.007
## 14 Whitelace 1 0.00967 0.00321 0.011
## # A tibble: 4 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 0.0000000150 0.0000000150 0.00251 0.960
## 2 Plant 6 0.000283 0.0000471 7.90 0.0000120
## 3 WormPresence:Plant 6 0.000126 0.0000210 3.52 0.00691
## 4 Residuals 40 0.000239 0.00000597 NA NA
if (!is.null(results_detailed[["IR"]])) {
print(results_detailed[["IR"]]$SummaryStats)
print(results_detailed[["IR"]]$ANOVA)
}
## # A tibble: 14 × 5
## Plant WormPresence Mean SD Median
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Chives 0 0.032 0.00173 0.033
## 2 Chives 1 0.0277 0.00569 0.026
## 3 Cilantro 0 0.0263 0.00404 0.024
## 4 Cilantro 1 0.0247 0.00153 0.025
## 5 Larkspur 0 0.0412 0.00729 0.042
## 6 Larkspur 1 0.0382 0.0125 0.04
## 7 Mulch 0 0.0474 0.0110 0.05
## 8 Mulch 1 0.0316 0.0119 0.034
## 9 Onion 0 0.0336 0.00879 0.03
## 10 Onion 1 0.0294 0.0105 0.029
## 11 Scallion 0 0.0337 0.00321 0.035
## 12 Scallion 1 0.0327 0.00115 0.032
## 13 Whitelace 0 0.017 0.00436 0.015
## 14 Whitelace 1 0.018 0.00173 0.019
## # A tibble: 4 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 0.000328 0.000328 4.65 0.0371
## 2 Plant 6 0.00271 0.000451 6.40 0.0000868
## 3 WormPresence:Plant 6 0.000398 0.0000664 0.943 0.475
## 4 Residuals 40 0.00282 0.0000704 NA NA
if (!is.null(results_detailed[["Man"]])) {
print(results_detailed[["Man"]]$SummaryStats)
print(results_detailed[["Man"]]$ANOVA)
}
## # A tibble: 14 × 5
## Plant WormPresence Mean SD Median
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Chives 0 0 0 0
## 2 Chives 1 0 0 0
## 3 Cilantro 0 0 0 0
## 4 Cilantro 1 0 0 0
## 5 Larkspur 0 0.0004 0.000548 0
## 6 Larkspur 1 0.0004 0.000548 0
## 7 Mulch 0 0.004 0.00548 0
## 8 Mulch 1 0.0004 0.000548 0
## 9 Onion 0 0.0022 0.00438 0
## 10 Onion 1 0.0004 0.000548 0
## 11 Scallion 0 0 0 0
## 12 Scallion 1 0 0 0
## 13 Whitelace 0 0 0 0
## 14 Whitelace 1 0 0 0
## # A tibble: 4 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 0.0000135 0.0000135 2.68 0.110
## 2 Plant 6 0.0000387 0.00000646 1.28 0.288
## 3 WormPresence:Plant 6 0.000027 0.0000045 0.893 0.509
## 4 Residuals 40 0.000202 0.00000504 NA NA
if (!is.null(results_detailed[["MO"]])) {
print(results_detailed[["MO"]]$SummaryStats)
print(results_detailed[["MO"]]$ANOVA)
}
## # A tibble: 14 × 5
## Plant WormPresence Mean SD Median
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Chives 0 0.0543 0.0544 0.034
## 2 Chives 1 0.00467 0.00451 0.005
## 3 Cilantro 0 0 0 0
## 4 Cilantro 1 0.00733 0.00153 0.007
## 5 Larkspur 0 0.0302 0.0371 0.01
## 6 Larkspur 1 0.0086 0.00313 0.008
## 7 Mulch 0 0.00196 0.00268 0
## 8 Mulch 1 0.00196 0.00268 0
## 9 Onion 0 0.00296 0.00270 0.0049
## 10 Onion 1 0.00298 0.00272 0.0049
## 11 Scallion 0 0.00167 0.00289 0
## 12 Scallion 1 0 0 0
## 13 Whitelace 0 0 0 0
## 14 Whitelace 1 0.0127 0.00651 0.013
## # A tibble: 4 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 0.000755 0.000755 2.57 0.117
## 2 Plant 6 0.00508 0.000846 2.88 0.0198
## 3 WormPresence:Plant 6 0.00444 0.000740 2.52 0.0366
## 4 Residuals 40 0.0117 0.000293 NA NA
if (!is.null(results_detailed[["SO"]])) {
print(results_detailed[["SO"]]$SummaryStats)
print(results_detailed[["SO"]]$ANOVA)
}
## # A tibble: 14 × 5
## Plant WormPresence Mean SD Median
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Chives 0 23.3 4.07 21.6
## 2 Chives 1 21.2 2.20 20.7
## 3 Cilantro 0 20.1 1.43 19.3
## 4 Cilantro 1 24.5 8.40 19.8
## 5 Larkspur 0 20.4 2.81 19
## 6 Larkspur 1 22.2 2.41 21.1
## 7 Mulch 0 18.1 3.15 18.9
## 8 Mulch 1 19.5 2.65 18.7
## 9 Onion 0 19.7 2.40 21
## 10 Onion 1 21.1 3.31 21
## 11 Scallion 0 23.6 4.71 22.8
## 12 Scallion 1 22.4 2.58 22.9
## 13 Whitelace 0 20.7 0.518 20.8
## 14 Whitelace 1 24.4 3.38 24.2
## # A tibble: 4 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 25.7 25.7 2.24 0.142
## 2 Plant 6 108. 18.0 1.57 0.180
## 3 WormPresence:Plant 6 51.4 8.56 0.748 0.615
## 4 Residuals 40 458. 11.5 NA NA
if (!is.null(results_detailed[["SU"]])) {
print(results_detailed[["SU"]]$SummaryStats)
print(results_detailed[["SU"]]$ANOVA)
}
## # A tibble: 14 × 5
## Plant WormPresence Mean SD Median
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Chives 0 8.45 3.71 9.10
## 2 Chives 1 7.67 4.49 6.65
## 3 Cilantro 0 5.19 1.31 5.51
## 4 Cilantro 1 2.83 0.273 2.97
## 5 Larkspur 0 6.54 1.99 5.96
## 6 Larkspur 1 5.92 1.48 5.00
## 7 Mulch 0 3.19 0.960 3
## 8 Mulch 1 2.71 1.41 2.28
## 9 Onion 0 6.05 1.42 6.03
## 10 Onion 1 5.79 2.69 5.28
## 11 Scallion 0 7.28 4.12 5.34
## 12 Scallion 1 5.76 1.33 5.24
## 13 Whitelace 0 8.22 4.23 6.10
## 14 Whitelace 1 6.25 1.83 7.15
## # A tibble: 4 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 13.2 13.2 2.35 0.133
## 2 Plant 6 148. 24.6 4.38 0.00171
## 3 WormPresence:Plant 6 7.06 1.18 0.209 0.972
## 4 Residuals 40 225. 5.62 NA NA
if (!is.null(results_detailed[["Zi"]])) {
print(results_detailed[["Zi"]]$SummaryStats)
print(results_detailed[["Zi"]]$ANOVA)
}
## # A tibble: 14 × 5
## Plant WormPresence Mean SD Median
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Chives 0 0.0377 0.00551 0.038
## 2 Chives 1 0.027 0.00265 0.028
## 3 Cilantro 0 0.0573 0.0129 0.061
## 4 Cilantro 1 0.0483 0.0170 0.047
## 5 Larkspur 0 0.0564 0.0303 0.045
## 6 Larkspur 1 0.0628 0.0313 0.065
## 7 Mulch 0 0.0652 0.0242 0.079
## 8 Mulch 1 0.048 0.0134 0.043
## 9 Onion 0 0.0946 0.0926 0.046
## 10 Onion 1 0.0664 0.0430 0.06
## 11 Scallion 0 0.0353 0.00850 0.035
## 12 Scallion 1 0.054 0.0154 0.05
## 13 Whitelace 0 0.0677 0.0516 0.049
## 14 Whitelace 1 0.0793 0.0214 0.088
## # A tibble: 4 × 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WormPresence 1 0.000492 0.000492 0.327 0.571
## 2 Plant 6 0.0117 0.00195 1.29 0.282
## 3 WormPresence:Plant 6 0.00336 0.000560 0.372 0.892
## 4 Residuals 40 0.0602 0.00150 NA NA
# ------------------------------------------------------------------------------------
# ------------------------------------------------------------------------------------
# ------------------------------------------------------------------------------------
# Function to analyze and plot data
analyze_and_plot_elements <- function(data) {
# Loop over each element
unique_elements <- unique(data$element)
plots <- list() # List to store ggplot objects
for (elem in unique_elements) {
# Filter data for the specific element
elem_data <- data %>% filter(element == elem)
# Calculate summary statistics
summary_stats <- elem_data %>%
group_by(Plant, WormPresence) %>%
summarise(
Mean = mean(amount, na.rm = TRUE),
SD = sd(amount, na.rm = TRUE),
.groups = 'drop'
) %>%
ungroup() %>%
mutate(WormPresence = factor(WormPresence, levels = c(0, 1), labels = c("No Worms", "Worms")))
# Generate plot
p <- ggplot(summary_stats, aes(x = Plant, y = Mean, fill = WormPresence)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.8), width = 0.7) +
geom_errorbar(aes(ymin = Mean - SD, ymax = Mean + SD), width = 0.25, position = position_dodge(width = 0.8)) +
facet_wrap(~WormPresence) +
labs(title = paste("Element:", elem),
y = "Mean Amount with SD",
x = "Plant Type") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Store plot in list
plots[[elem]] <- p
}
return(plots)
}
# Usage
plots <- analyze_and_plot_elements(long_dat)
# To display plots for a specific element, e.g., "Ph"
if (!is.null(plots[["Ph"]])) {
print(plots[["Ph"]])
}
if (!is.null(plots[["SS"]])) {
print(plots[["SS"]])
}
if (!is.null(plots[["NitN"]])) {
print(plots[["NitN"]])
}
if (!is.null(plots[["AmoN"]])) {
print(plots[["AmoN"]])
}
if (!is.null(plots[["CA"]])) {
print(plots[["CA"]])
}
if (!is.null(plots[["Po"]])) {
print(plots[["Po"]])
}
if (!is.null(plots[["Mag"]])) {
print(plots[["Mag"]])
}
if (!is.null(plots[["Pho"]])) {
print(plots[["Pho"]])
}
if (!is.null(plots[["AL"]])) {
print(plots[["AL"]])
}
if (!is.null(plots[["BO"]])) {
print(plots[["BO"]])
}
if (!is.null(plots[["CO"]])) {
print(plots[["CO"]])
}
if (!is.null(plots[["IR"]])) {
print(plots[["IR"]])
}
if (!is.null(plots[["Man"]])) {
print(plots[["MAn"]])
}
## NULL
if (!is.null(plots[["MO"]])) {
print(plots[["MO"]])
}
if (!is.null(plots[["SO"]])) {
print(plots[["SO"]])
}
if (!is.null(plots[["SU"]])) {
print(plots[["SU"]])
}
if (!is.null(plots[["Zi"]])) {
print(plots[["Zi"]])
}
# ------------------------------------------------------------------------------------
# ------------------------------------------------------------------------------------
# ------------------------------------------------------------------------------------
# Function to plot the summary statistics and ANOVA results
plot_element_results <- function(results) {
plots <- list() # List to store ggplot objects
# Loop over each element in the results list
for (elem in names(results)) {
summary_stats <- results[[elem]]$SummaryStats
aov_results <- results[[elem]]$ANOVA
# Ensure WormPresence is a factor with proper labels
summary_stats$WormPresence <- factor(summary_stats$WormPresence, levels = c(0, 1), labels = c("No Worm", "Worm"))
# Create the plot for summary statistics
p <- ggplot(summary_stats, aes(x = Plant, y = Mean, fill = WormPresence)) +
geom_bar(stat = "identity", position = position_dodge(), width = 0.7, color="black",size=1) +
geom_errorbar(aes(ymin = Mean - SD, ymax = Mean + SD), width = 0.25, position = position_dodge(0.7)) +
geom_point(position = position_jitter(width = 0.2, height = 0), color = "black", size = 1)+
facet_wrap(~WormPresence) +
labs(title = paste("Element:", elem, "\nANOVA p-value:", min(aov_results$p.value, na.rm = TRUE)),
x = "Plant Type",
y = "Mean Amount with SD",
fill = "Worm Presence") +
scale_fill_manual(values = c("No Worm" = "thistle", "Worm" = "goldenrod"), # Specifying actual colors for "No Worm" and "Worm"
labels = c("No Worm", "Worm")) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Store the plot
plots[[elem]] <- p
}
return(plots)
}
# Rerun the plotting function with the corrected settings
plots <- plot_element_results(results_detailed)
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# Display plot for a specific element, e.g., "Ph"
if (!is.null(plots[["Ph"]])) {
print(plots[["Ph"]])
}
if (!is.null(plots[["SS"]])) {
print(plots[["SS"]])
}
if (!is.null(plots[["NitN"]])) {
print(plots[["NitN"]])
}
if (!is.null(plots[["AmoN"]])) {
print(plots[["AmoN"]])
}
if (!is.null(plots[["CA"]])) {
print(plots[["CA"]])
}
if (!is.null(plots[["Po"]])) {
print(plots[["Po"]])
}
if (!is.null(plots[["Mag"]])) {
print(plots[["MAg"]])
}
## NULL
if (!is.null(plots[["Pho"]])) {
print(plots[["Pho"]])
}
if (!is.null(plots[["AL"]])) {
print(plots[["AL"]])
}
if (!is.null(plots[["BO"]])) {
print(plots[["BO"]])
}
if (!is.null(plots[["CO"]])) {
print(plots[["CO"]])
}
if (!is.null(plots[["IR"]])) {
print(plots[["IR"]])
}
if (!is.null(plots[["Man"]])) {
print(plots[["Man"]])
}
if (!is.null(plots[["MO"]])) {
print(plots[["MO"]])
}
if (!is.null(plots[["SO"]])) {
print(plots[["SO"]])
}
if (!is.null(plots[["SO"]])) {
print(plots[["SO"]])
}
if (!is.null(plots[["SU"]])) {
print(plots[["SU"]])
}
if (!is.null(plots[["Zi"]])) {
print(plots[["Zi"]])
}
# Display the plot for 'Zi', if it exists
if (!is.null(plots[["Zi"]])) {
print(plots[["Zi"]])
} else {
print("No plot available for 'Zi'. Check if 'Zi' is a valid element in your results.")
}
